Exploring the Methods of Differentiation to Support English Language Learners by Elementary Teachers in the Mainstream Classroom
Bibliographic record
Abstract
The aim of this qualitative research study was to investigate strategies, outcomes, challenges, and resources of mainstreaming English language learners in elementary classrooms. The main research question that guided this study was: How is a small sample of Ontario elementary school teachers supporting the development of English proficiency and academic achievement for English language learners across subject areas? Semi-structured interviews with two elementary teachers were utilized to collect data. Findings suggest that differentiation in teaching and assessment is an effective strategy for supporting ELLs, which results in their increased achievement of classroom tasks as well as voluntary peer support from the English-speaking students. As well, teachers encounter challenges related to limited interaction between schools and families and the slow student learning process. Findings also suggest that a supportive school environment and professional resources are important in assisting teachers in supporting ELLs in a mainstream classroom. The implications of these findings suggest that an inclusive environment is conducive to improvement in ELLs’ learning outcome and social integration. Also, the research findings indicate that ELLs might demonstrate slow learning processes or low academic achievement initially because of their limited and developing English skills; such limitations may be misdiagnosed as learning disabilities due to educators’ insufficient knowledge of second language acquisition. This can result in ELLs with limited English skills being misplaced in special education programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".